Spark Talks: A Text-As-Data Approach for Using Open-Ended Responses as Manipulation Checks

DESCRIPTION: Participants that complete online surveys and experiments may be inattentive, which can hinder researchers’ ability to draw substantive or causal inferences. Closed-ended manipulation checks are either correct or incorrect, which allows participants to more easily guess and reduces the potential variation in attention between respondents. Alternatively, Dr. Ziegler developed an automatic and standardized methodology to measure attention that relies on the text that respondents provide in an open-ended manipulation check. He will also discuss how to diagnose the impact of inattentive workers on the overall results, including how to assess the average treatment effect of those respondents that likely received the treatment.

Host BIO: Jeff Ziegler is an Assistant Professor in Political Science and Data Science at Trinity College Dublin. In his research, he extends computational and quantitative methods to the social sciences, focusing on multi-media data (text, audio, images) and experiments. He applies these techniques to study questions related to political and psychological behavior.

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